Interdisciplinarity in Adapted Physical Activity
Bibliographic record
Abstract
It is commonly accepted that inquiry in adapted physical activity involves the use of different disciplines to address questions. It is often advanced today that complex problems of the kind frequently encountered in adapted physical activity require a combination of disciplines for their solution. At the present time, individual research questions in adapted physical activity are most often developed and pursued by researchers from a single discipline despite incentives to the contrary. However, the inclusion of multiple disciplines to address research questions raises a number of challenges. A major one is effective communication. The language related to the use of multiple disciplines is often used loosely. Key terms, such as multi-disciplinary, interdisciplinary, transdisciplinary, and cross-disciplinary, are often used interchangeably. We introduce the technical meaning of these terms and outline some key epistemic challenges to communication across disciplines and highlight the importance of willingness, on the part of researchers, to carefully listen to each other.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.076 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.063 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".